code: def test_gridsearchcv_refit(u1_ml100k):
      """Test refit function of GridSearchCV."""
  
      data_file = os.path.join(
          os.path.dirname(__file__), "./u1_ml100k_test"
      )
      data = Dataset.load_from_file(data_file, Reader("ml-100k"))
  
      param_grid = {
          "n_epochs": [5],
          "lr_all": [0.002, 0.005],
          "reg_all": [0.4, 0.6],
          "n_factors": [2],
      }
  
      # assert gs.fit() and gs.test will use best estimator for mae (first
      # appearing in measures)
      gs = GridSearchCV(
          SVD, param_grid, measures=["mae", "rmse"], cv=2, refit=True
      )
      gs.fit(data)
      gs_preds = gs.test(data.construct_testset(data.raw_ratings))
      mae_preds = gs.best_estimator["mae"].test(
          data.construct_testset(data.raw_ratings)
      )
      assert gs_preds == mae_preds
  
      # assert gs.fit() and gs.test will use best estimator for rmse
      gs = GridSearchCV(
          SVD, param_grid, measures=["mae", "rmse"], cv=2, refit="rmse"
      )
      gs.fit(data)
      gs_preds = gs.test(data.construct_testset(data.raw_ratings))
      rmse_preds = gs.best_estimator["rmse"].test(
          data.construct_testset(data.raw_ratings)
      )
      assert gs_preds == rmse_preds
      # test that predict() can be called
      gs.predict(2, 4)
  
      # assert test() and predict() cannot be used when refit is false
      gs = GridSearchCV(
          SVD, param_grid, measures=["mae", "rmse"], cv=2, refit=False
      )
      gs.fit(data)
      with pytest.raises(ValueError):
          gs_preds = gs.test(data.construct_testset(data.raw_ratings))
      with pytest.raises(ValueError):
          gs.predict("1", "2")
  
      # test that error is raised if used with load_from_folds
      gs = GridSearchCV(
          SVD, param_grid, measures=["mae", "rmse"], cv=2, refit=True
      )
      with pytest.raises(ValueError):
          gs.fit(u1_ml100k)
  
file_path: Surprise/tests/test_search.py
imports: import os
  import numpy
  import pytest
  from scipy.stats import randint, uniform
  from surprise import Dataset, Reader, SVD
  from surprise.model_selection import cross_validate, GridSearchCV, KFold, PredefinedKFold, RandomizedSearchCV
label_desc: Use image node_id 6 for calling a global function with example usage: test_gridsearchcv_refit(u1_ml100k) without return types